no code implementations • 19 Feb 2024 • Souvik Kundu, Anthony Sarah, Vinay Joshi, Om J Omer, Sreenivas Subramoney
With the recent growth in demand for large-scale deep neural networks, compute in-memory (CiM) has come up as a prominent solution to alleviate bandwidth and on-chip interconnect bottlenecks that constrain Von-Neuman architectures.
no code implementations • 10 Feb 2021 • Vinay Joshi, Wangxin He, Jae-sun Seo, Bipin Rajendran
We propose a hybrid in-memory computing (HIC) architecture for the training of DNNs on hardware accelerators that results in memory-efficient inference and outperforms baseline software accuracy in benchmark tasks.
no code implementations • 25 Mar 2020 • Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo, Irem Boybat, Christophe Piveteau, Abu Sebastian, Bipin Rajendran, Evangelos Eleftheriou
Strategies to improve the efficiency of MVM computation in hardware have been demonstrated with minimal impact on training accuracy.
no code implementations • 7 Jun 2019 • Vinay Joshi, Manuel Le Gallo, Irem Boybat, Simon Haefeli, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, Evangelos Eleftheriou
In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within memory units by exploiting the physical attributes of memory devices.
Emerging Technologies